Professor of Computer Science
Head, MIT Computational Biology Group
Institute Member, Broad Institute of MIT and Harvard
Member, MIT Computer Science and Artificial Intelligence Lab
- Electrical Engineering and Computer Science (EECS)
Perhaps the greatest surprise of human genome-wide association studies (GWAS) is that 90% of disease-associated regions do not affect proteins directly, but instead lie in non-coding regions with putative gene-regulatory roles. This has increased the urgency of understanding the non-coding genome, as a key component of understanding human disease. To address this challenge, we generated maps of genomic control elements across 127 primary human tissues and cell types, and tissue-specific regulatory networks linking these elements to their target genes and their regulators. We have used these maps and circuits to understand how human genetic variation contributes to disease and cancer, providing an unbiased view of disease genetics and sometimes re-shaping our understanding of common disorders. For example, we find evidence that genetic variants contributing to Alzheimer’s disease act primarily through immune processes, rather than neuronal processes. We also find that the strongest genetic association with obesity acts via a master switch controlling energy storage vs. energy dissipation in our adipocytes, rather than through the control of appetite in the brain. We also combine genetic information with regulatory annotations and epigenomic variation across patients and healthy controls to discover new disease genes and regions with roles in Alzheimer’s disease, heart disease, and prostate cancer. Lastly, we manipulate these circuits by genome editing and gene targeting in human cells and in mice, demonstrating tissue-autonomous therapeutic avenues in Alzheimer’s disease, obesity, and cancer. These results provide a roadmap for translating genetic findings into mechanistic insights and ultimately therapeutic treatments for complex disease and cancer.